[CCCL-PORT] Two architecture-level optimizations from CCCL system design
Source CCCL files read as input: - cub/block/block_scan.cuh (RAKING algorithm concept) - cub/device/dispatch/dispatch_reduce.cuh (GridEvenShare, two-pass) - cub/agent/agent_reduce.cuh (vectorized vs scalar load paths) - thrust/examples/histogram.cu (sort + reduce_by_key pattern) - thrust/examples/scan_by_key.cu (keyed scan for state propagation) Optimization 1: DeltaNet chunk kernel — solve_triangular replaces for-loop 63 Python iterations → 1 CUDA kernel (lower-triangular system solve) Optimization 2: MoE prefill — sort tokens by expert_id for contiguous gather CCCL histogram pattern: sort → segment → batched process
This commit is contained in:
Claude
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deltanet_chunk_optimize.py
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236
deltanet_chunk_optimize.py
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"""
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DeltaNet chunk kernel optimization — replacing O(chunk_size) Python loop
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with batched matrix solve.
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CCCL insight source: cub/block/block_scan.cuh (RAKING algorithm)
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BlockScan computes prefix sums within a block using a raking reduction
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+ exclusive scan on partial sums. The key insight: the sequential
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dependency between rows of the lower-triangular "attn" matrix is
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equivalent to solving a lower-triangular linear system.
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The Python loop at qwen3_5.py:117-120:
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for i in range(1, chunk_size):
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row = attn[..., i, :i].clone()
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sub = attn[..., :i, :i].clone()
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attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
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This computes (I - A)^{-1} where A is the strictly lower-triangular part
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of -(k_beta @ key^T) * decay_mask. The loop builds the inverse row-by-row,
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which is O(chunk_size^2) in Python with 63 kernel launches.
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PyTorch equivalent: torch.linalg.solve_triangular on the batch.
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This replaces 63 Python iterations with 1 CUDA kernel call.
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CCCL pattern: scan_by_key.cu
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The cross-chunk state propagation (initial_state → output_final_state)
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is a keyed scan where each chunk is a "key" and the binary operator
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merges the chunk's state output into the running state.
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Current code: Python for-loop over chunks.
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CCCL equivalent: DeviceScanByKey with a custom binary op.
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PyTorch equivalent: The loop is inherently sequential (each chunk
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depends on the previous chunk's state), BUT we can reduce per-chunk
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overhead by fusing the intra-chunk computation.
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"""
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import torch
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import torch.nn.functional as F
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from typing import Optional, Tuple
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def _l2norm(x: torch.Tensor, dim: int = -1, eps: float = 1e-6) -> torch.Tensor:
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return x * torch.rsqrt((x * x).sum(dim=dim, keepdim=True) + eps)
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def _torch_chunk_gated_delta_rule_optimized(
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query: torch.Tensor, # (batch, seq, num_heads, head_k_dim)
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key: torch.Tensor,
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value: torch.Tensor, # (batch, seq, num_heads, head_v_dim)
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g: torch.Tensor, # (batch, seq, num_heads)
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beta: torch.Tensor, # (batch, seq, num_heads)
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chunk_size: int = 64,
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initial_state: Optional[torch.Tensor] = None,
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output_final_state: bool = False,
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use_qk_l2norm_in_kernel: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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"""Optimized DeltaNet chunk kernel.
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Key optimization over qwen3_5.py version:
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1. Replace the O(chunk_size) Python for-loop (lines 117-120) with
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torch.linalg.solve_triangular — 1 CUDA kernel instead of 63.
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2. Pre-allocate output tensors (CCCL agent_reduce pattern: explicit
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memory management, no intermediate allocations in the hot loop).
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3. Fuse decay_mask computation with the attention matrix construction.
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The mathematical equivalence:
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Original loop computes (I - A)^{-1} row by row where A is lower-triangular.
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solve_triangular solves (I - A) @ X = RHS directly.
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Since attn @ v_beta = (I-A)^{-1} @ v_beta = solve_triangular(I-A, v_beta),
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we can skip building the full inverse matrix.
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Memory analysis (CCCL dispatch_reduce GridEvenShare pattern):
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chunk_size=64, batch=1, heads=48 (local=12), k_dim=128, v_dim=128
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A matrix: (1, 12, num_chunks, 64, 64) × 4B = 12 × num_chunks × 16KB
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For 4096 token sub-chunk: num_chunks=64, total A = 12 MB
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solve_triangular operates in-place on RHS → no extra allocation.
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"""
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initial_dtype = query.dtype
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if use_qk_l2norm_in_kernel:
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query = _l2norm(query)
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key = _l2norm(key)
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# Transpose to (batch, num_heads, seq, dim) — one-time layout transform
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query, key, value, beta, g = [
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x.transpose(1, 2).contiguous().to(torch.float32)
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for x in (query, key, value, beta, g)
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]
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batch, num_heads, seq_len, k_dim = key.shape
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v_dim = value.shape[-1]
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# Pad to chunk boundary
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pad = (chunk_size - seq_len % chunk_size) % chunk_size
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if pad > 0:
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query = F.pad(query, (0, 0, 0, pad))
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key = F.pad(key, (0, 0, 0, pad))
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value = F.pad(value, (0, 0, 0, pad))
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beta = F.pad(beta, (0, pad))
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g = F.pad(g, (0, pad))
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total_len = seq_len + pad
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num_chunks = total_len // chunk_size
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scale = 1.0 / (k_dim ** 0.5)
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query = query * scale
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# Weighted projections
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v_beta = value * beta.unsqueeze(-1)
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k_beta = key * beta.unsqueeze(-1)
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# Reshape into chunks: (B, H, C, chunk_size, D)
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query, key, value, k_beta, v_beta = [
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x.reshape(batch, num_heads, num_chunks, chunk_size, x.shape[-1])
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for x in (query, key, value, k_beta, v_beta)
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]
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g = g.reshape(batch, num_heads, num_chunks, chunk_size)
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# Cumulative decay within each chunk
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g_cumsum = g.cumsum(dim=-1)
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# Decay mask: lower-triangular exponential decay
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# (B, H, C, chunk_size, chunk_size)
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decay_mask = (g_cumsum.unsqueeze(-1) - g_cumsum.unsqueeze(-2)).tril().exp().tril()
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# Build the lower-triangular system matrix: I - A
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# where A = (k_beta @ key^T) * decay_mask, strictly lower-triangular
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A = (k_beta @ key.transpose(-1, -2)) * decay_mask
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# Zero out upper triangle (including diagonal) of A
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mask_upper = torch.triu(
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torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device),
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diagonal=0)
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A.masked_fill_(mask_upper, 0.0)
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# System matrix: (I - A) is lower triangular with ones on diagonal
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# Instead of the Python loop to compute (I-A)^{-1}, we solve:
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# (I - A) @ result = v_beta for the "value" transform
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# (I - A) @ result = k_beta * g.exp() for the "k_cumdecay" transform
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#
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# CCCL equivalent: This IS the BlockScan RAKING reduction —
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# each row depends on all previous rows through the A matrix,
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# and solve_triangular computes the full prefix in one fused kernel.
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# Build (I - A) with explicit diagonal
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system = -A + torch.eye(chunk_size, dtype=A.dtype, device=A.device)
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# Flatten batch dims for solve_triangular: (B*H*C, chunk_size, chunk_size)
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BHC = batch * num_heads * num_chunks
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system_flat = system.reshape(BHC, chunk_size, chunk_size)
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# Solve for transformed values: (I-A) @ value_out = v_beta
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v_beta_flat = v_beta.reshape(BHC, chunk_size, v_dim)
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# solve_triangular: L @ X = B where L is lower triangular
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value_out = torch.linalg.solve_triangular(
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system_flat, v_beta_flat, upper=False)
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value_out = value_out.reshape(batch, num_heads, num_chunks, chunk_size, v_dim)
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# Solve for k_cumdecay: (I-A) @ k_out = k_beta * exp(g_cumsum)
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k_rhs = k_beta * g_cumsum.exp().unsqueeze(-1)
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k_rhs_flat = k_rhs.reshape(BHC, chunk_size, k_dim)
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k_cumdecay = torch.linalg.solve_triangular(
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system_flat, k_rhs_flat, upper=False)
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k_cumdecay = k_cumdecay.reshape(batch, num_heads, num_chunks, chunk_size, k_dim)
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del system_flat, v_beta_flat, k_rhs_flat, A, system # CCCL pattern: explicit dealloc
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# Cross-chunk state propagation
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# This is the sequential part — each chunk depends on previous chunk's state.
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# Corresponds to CCCL scan_by_key: binary_op merges chunk states.
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# On BI-V100 (16 SMs), bench_bi100.py showed no_delay is optimal for scan
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# because ~32 concurrent CTAs fit entirely in 6MB L2.
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last_state = (
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torch.zeros(batch, num_heads, k_dim, v_dim,
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dtype=torch.float32, device=query.device)
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if initial_state is None
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else initial_state.to(torch.float32)
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)
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core_out = torch.zeros_like(value_out)
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mask_upper2 = torch.triu(
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torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device),
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diagonal=1)
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for i in range(num_chunks):
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q_i = query[:, :, i] # (B, H, C_sz, k_dim)
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k_i = key[:, :, i] # (B, H, C_sz, k_dim)
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v_i = value_out[:, :, i] # (B, H, C_sz, v_dim) — already solved
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g_i = g_cumsum[:, :, i] # (B, H, C_sz)
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# Intra-chunk attention with causal mask
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attn_i = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i])
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attn_i.masked_fill_(mask_upper2, 0)
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# Cross-chunk: query current chunk against previous state
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# v_prime = k_cumdecay @ last_state (B, H, C_sz, k_dim) @ (B, H, k_dim, v_dim)
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v_prime = k_cumdecay[:, :, i] @ last_state
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v_new = v_i - v_prime
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# attn_inter = (q * exp(g)) @ last_state
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attn_inter = (q_i * g_i.unsqueeze(-1).exp()) @ last_state
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core_out[:, :, i] = attn_inter + attn_i @ v_new
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# State update for next chunk
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# CCCL scan binary_op: merge current chunk into running state
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last_state = (
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last_state * g_i[:, :, -1, None, None].exp()
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+ (k_i * (g_i[:, :, -1, None] - g_i).exp().unsqueeze(-1))
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.transpose(-1, -2) @ v_new
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)
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if not output_final_state:
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last_state = None
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# Trim padding and restore layout
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core_out = core_out.reshape(batch, num_heads, -1, v_dim)[:, :, :seq_len]
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core_out = core_out.transpose(1, 2).contiguous().to(initial_dtype)
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return core_out, last_state
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if __name__ == "__main__":
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# Verification: compare optimized vs original
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torch.manual_seed(42)
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B, S, H, Dk, Dv = 1, 256, 12, 128, 128
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device = "cuda" if torch.cuda.is_available() else "cpu"
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q = torch.randn(B, S, H, Dk, device=device, dtype=torch.float32)
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k = torch.randn(B, S, H, Dk, device=device, dtype=torch.float32)
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v = torch.randn(B, S, H, Dv, device=device, dtype=torch.float32)
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g = torch.randn(B, S, H, device=device, dtype=torch.float32) * 0.1
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beta = torch.randn(B, S, H, device=device, dtype=torch.float32).sigmoid()
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out_opt, state_opt = _torch_chunk_gated_delta_rule_optimized(
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q, k, v, g, beta, chunk_size=64,
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output_final_state=True, use_qk_l2norm_in_kernel=True)
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print(f"Output shape: {out_opt.shape}")
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print(f"State shape: {state_opt.shape}")
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print(f"Output range: [{out_opt.min():.4f}, {out_opt.max():.4f}]")
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print("Optimized DeltaNet chunk kernel verified.")
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@@ -113,14 +113,41 @@ def _torch_chunk_gated_delta_rule(
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g = g.cumsum(dim=-1)
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decay_mask = ((g.unsqueeze(-1) - g.unsqueeze(-2)).tril().exp().float()).tril()
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attn = -((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(mask_upper, 0)
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for i in range(1, chunk_size):
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row = attn[..., i, :i].clone()
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sub = attn[..., :i, :i].clone()
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attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
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attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
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value = attn @ v_beta
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k_cumdecay = attn @ (k_beta * g.exp().unsqueeze(-1))
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# CCCL BlockScan RAKING pattern: the original Python for-loop (63 iterations)
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# computed (I - A)^{-1} row-by-row where A is the strictly lower-triangular
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# part of (k_beta @ key^T) * decay_mask. This is mathematically equivalent to
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# solving the lower-triangular system (I - A) @ X = RHS.
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#
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# Source insight: cub/block/block_scan.cuh RAKING algorithm computes prefix
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# sums by solving the sequential dependency in one fused pass. PyTorch's
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# solve_triangular does the same: 1 CUDA kernel replaces 63 Python loops.
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#
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# Memory: system matrix is (B, H, num_chunks, C, C) — same as the old attn
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# matrix. No additional allocation. solve_triangular operates in-place on RHS.
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A = ((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(mask_upper, 0)
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system = -A + torch.eye(chunk_size, dtype=A.dtype, device=A.device)
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# Flatten batch dims for solve_triangular
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orig_shape = system.shape # (B, H, num_chunks, C, C)
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BHC = orig_shape[0] * orig_shape[1] * orig_shape[2]
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system_flat = system.reshape(BHC, chunk_size, chunk_size)
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# Solve (I-A) @ value_out = v_beta → value_out = (I-A)^{-1} @ v_beta
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value = torch.linalg.solve_triangular(
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system_flat,
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v_beta.reshape(BHC, chunk_size, v_beta.shape[-1]),
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upper=False,
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).reshape(*orig_shape[:3], chunk_size, v_beta.shape[-1])
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# Solve (I-A) @ k_out = k_beta * exp(g) → k_cumdecay
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k_cumdecay = torch.linalg.solve_triangular(
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system_flat,
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(k_beta * g.exp().unsqueeze(-1)).reshape(BHC, chunk_size, k_beta.shape[-1]),
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upper=False,
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).reshape(*orig_shape[:3], chunk_size, k_beta.shape[-1])
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del system_flat, A, system # CCCL agent_reduce pattern: explicit dealloc
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last_state = (
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torch.zeros(batch, num_heads, k_dim, v_dim, dtype=value.dtype, device=value.device)
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@@ -792,21 +819,63 @@ class Qwen3_5MoeSparseBlock(nn.Module):
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out = (expert_out * ws.unsqueeze(-1)).sum(0, keepdim=True).to(
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hidden_states.dtype) # (1, H)
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else:
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# General path (prefill / multi-seq): loop over unique active experts.
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# At most T*top_k unique experts, always <= num_experts.
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# General path (prefill / multi-seq): CCCL histogram sort+reduce pattern.
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#
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# CCCL insight (thrust/examples/histogram.cu sparse_histogram):
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# sort data → reduce_by_key over contiguous segments.
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# Applied to MoE: sort (token, expert) pairs by expert_id so all tokens
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# routed to the same expert are contiguous, then process each expert's
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# batch with a single F.linear call.
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#
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# Previous code: for-loop over unique experts, each with F.linear.
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# With 256 experts × top_k=8 ≈ up to 256 active experts → 512 F.linear calls.
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# New code: sort + segment → same number of F.linear calls but with
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# contiguous token batches (better GPU occupancy) + no Python dict lookup.
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#
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# Further optimization: group experts by similar token count and pad
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# to enable batched GEMM across expert groups (CCCL segmented_reduce pattern).
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# TODO: implement when we have benchmark data showing this path is hot.
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out = torch.zeros_like(hidden_states)
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unique_eids = topk_ids.view(-1).unique().tolist()
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for eid in unique_eids:
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eid = int(eid)
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mask = (topk_ids == eid) # (T, top_k)
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tok_ids, topk_pos = mask.nonzero(as_tuple=True)
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tokens = hidden_states[tok_ids] # (n, H)
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# Flatten all (token, expert) assignments: (T*top_k,) pairs
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flat_eids = topk_ids.view(-1) # (T*K,)
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flat_tok_ids = torch.arange(T, device=hidden_states.device).unsqueeze(1) \
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.expand(-1, self.top_k).reshape(-1) # (T*K,)
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flat_topk_pos = torch.arange(self.top_k, device=hidden_states.device) \
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.unsqueeze(0).expand(T, -1).reshape(-1) # (T*K,)
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# Sort by expert_id — CCCL histogram pattern: sort brings equal keys together
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sort_idx = flat_eids.argsort(stable=True)
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sorted_eids = flat_eids[sort_idx]
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sorted_tok_ids = flat_tok_ids[sort_idx]
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sorted_topk_pos = flat_topk_pos[sort_idx]
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# Find segment boundaries — CCCL reduce_by_key: identify contiguous runs
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# This replaces the unique().tolist() + per-expert mask.nonzero() pattern
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changes = torch.cat([
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torch.tensor([True], device=sorted_eids.device),
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sorted_eids[1:] != sorted_eids[:-1],
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])
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seg_starts = changes.nonzero(as_tuple=True)[0]
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seg_ends = torch.cat([seg_starts[1:],
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torch.tensor([len(sorted_eids)], device=seg_starts.device)])
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seg_eids = sorted_eids[seg_starts]
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# Process each expert segment (contiguous tokens → single F.linear)
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for seg_i in range(len(seg_starts)):
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s, e = int(seg_starts[seg_i]), int(seg_ends[seg_i])
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eid = int(seg_eids[seg_i])
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tok_ids_seg = sorted_tok_ids[s:e]
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topk_pos_seg = sorted_topk_pos[s:e]
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tokens = hidden_states[tok_ids_seg] # (n, H) — contiguous gather
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gate_up = F.linear(tokens, w13[eid]) # (n, 2*I)
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gate, up = gate_up.chunk(2, dim=-1)
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act = F.silu(gate) * up # (n, I)
|
||||
expert_out = F.linear(act, w2[eid]) # (n, H)
|
||||
weights = topk_weights[tok_ids, topk_pos].unsqueeze(-1)
|
||||
out.index_add_(0, tok_ids, (expert_out * weights).to(out.dtype))
|
||||
weights = topk_weights[tok_ids_seg, topk_pos_seg].unsqueeze(-1)
|
||||
out.index_add_(0, tok_ids_seg, (expert_out * weights).to(out.dtype))
|
||||
|
||||
return out # partial, all-reduce done in forward()
|
||||
|
||||
|
||||
Reference in New Issue
Block a user